Hyperspectral image restoration using framelet-regularized low-rank nonnegative matrix factorization

Applied Mathematical Modelling - Tập 63 - Trang 128-147 - 2018
Yong Chen1, Ting-Zhu Huang1, Xi-Le Zhao1, Liang-Jian Deng1
1School of Mathematical Sciences/Resrarch Center for Image and Vision Computing, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, PR China

Tài liệu tham khảo

Lewis, 2011, Using hyperspectral imagery to estimate forest floor consumption from wildfire in boreal forests of Alaska, USA, Int. J. Wildland Fire, 20, 255, 10.1071/WF09081 Tiwari, 2011, An assessment of independent component analysis for detection of military targets from hyperspectral images, Int. J. Appl. Earth Observ. Geoinf., 13, 730, 10.1016/j.jag.2011.03.007 Xia, 2018, Random forest ensembles and extended multiextinction profiles for hyperspectral image classification, IEEE Trans. Geosci. Remote Sens., 56, 202, 10.1109/TGRS.2017.2744662 Iordache, 2012, Total variation spatial regularization for sparse hyperspectral unmixing, IEEE Trans. Geosci. Remote Sens., 50, 4484, 10.1109/TGRS.2012.2191590 Tarabalka, 2010, Segmentation and classification of hyperspectral images using watershed transformation, Pattern Recognit., 43, 2367, 10.1016/j.patcog.2010.01.016 Yokoya, 2015, Object detection based on sparse representation and hough voting for optical remote sensing imagery, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 8, 2053, 10.1109/JSTARS.2015.2404578 Elad, 2006, Image denoising via sparse and redundant representations over learned dictionaries, IEEE Trans. Image Process., 15, 3736, 10.1109/TIP.2006.881969 Dabov, 2007, Image denoising by sparse 3-D transform-domain collaborative filtering, IEEE Trans. Image Process., 16, 2080, 10.1109/TIP.2007.901238 Green, 1988, A transformation for ordering multispectral data in terms of image quality with implications for noise removal, IEEE Trans. Geosci. Remote Sens., 26, 65, 10.1109/36.3001 Othman, 2006, Noise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage, IEEE Trans. Geosci. Remote Sens., 44, 397, 10.1109/TGRS.2005.860982 Chen, 2008, Simultaneous dimensionality reduction and denoising of hyperspectral imagery using bivariate wavelet shrinking and principal component analysis, Can. J. Remote Sens., 34, 447, 10.5589/m08-058 Ye, 2015, Multitask sparse nonnegative matrix factorization for joint spectral-spatial hyperspectral imagery denoising, IEEE Trans. Geosci. Remote Sens., 53, 2621, 10.1109/TGRS.2014.2363101 Zhao, 2015, Hyperspectral image denoising via sparse representation and low-rank constraint, IEEE Trans. Geosci. Remote Sens., 53, 296, 10.1109/TGRS.2014.2321557 Yang, 2016, Coupled sparse denoising and unmixing with low-rank constraint for hyperspectral image, IEEE Trans. Geosci. Remote Sens., 54, 1818, 10.1109/TGRS.2015.2489218 Chen, 2011, Denoising of hyperspectral imagery using principal component analysis and wavelet shrinkage, IEEE Trans. Geosci. Remote Sens., 49, 973, 10.1109/TGRS.2010.2075937 Maggioni, 2013, Nonlocal transform-domain filter for volumetric data denoising and reconstruction, IEEE Trans. Image Process., 22, 119, 10.1109/TIP.2012.2210725 Karami, 2011, Noise reduction of hyperspectral images using kernel non-negative tucker decomposition, IEEE J. Sel. Top. Signal Process., 5, 487, 10.1109/JSTSP.2011.2132692 Guo, 2013, Hyperspectral image noise reduction based on rank-1 tensor decomposition, J. Photogramm. Remote Sens., 83, 50, 10.1016/j.isprsjprs.2013.06.001 Zhang, 2014, Hyperspectral image restoration using low-rank matrix recovery, IEEE Trans. Geosci. Remote Sens., 52, 4729, 10.1109/TGRS.2013.2284280 He, 2016, Total-variation-regularized low-rank matrix factorization for hyperspectral image restoration, IEEE Trans. Geosci. Remote Sens., 54, 178, 10.1109/TGRS.2015.2452812 Xie, 2016, Hyperspectral image restoration via iteratively regularized weighted schatten p-norm minimization, IEEE Trans. Geosci. Remote Sens., 54, 4642, 10.1109/TGRS.2016.2547879 Chen, 2017, Denoising of hyperspectral images using nonconvex low rank matrix approximation, IEEE Trans. Geosci. Remote Sens., 55, 5366, 10.1109/TGRS.2017.2706326 Wu, 2017, Structure tensor total variation-regularized weighted nuclear norm minimization for hyperspectral image mixed denoising, Signal Process., 131, 202, 10.1016/j.sigpro.2016.07.031 Chen, 2018, Denoising hyperspectral image with non-i.i.d. noise structure, IEEE Trans. Cybern., 48, 1054, 10.1109/TCYB.2017.2677944 Aggarwal, 2016, Hyperspectral image denoising using spatio-spectral total variation, IEEE Geosci. Remote Sens. Lett., 13, 442 Li, 2015, Hyperspectral image denoising using the robust low-rank tensor recovery, J. Opt. Soc. Am. A Opt. Image Sci. Vis., 32, 1604, 10.1364/JOSAA.32.001604 Fan, 2017, Hyperspectral image restoration using low-rank tensor recovery, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 10, 4589, 10.1109/JSTARS.2017.2714338 Wang, 2018, Hyperspectral image restoration via total variation regularized low-rank tensor decomposition, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 11, 1227, 10.1109/JSTARS.2017.2779539 Zhao, 2013, Total variation structured total least squares method for image restoration, SIAM J. Sci. Comput., 35, B1304, 10.1137/130915406 Chen, 2017, Stripe noise removal of remote sensing images by total variation regularization and group sparsity constraint, Remote Sens., 9, 559, 10.3390/rs9060559 Zhao, 2014, A new convex optimization model for multiplicative noise and blur removal, Siam J. Imaging Sci., 7, 456, 10.1137/13092472X Mei, 2018, Cauchy noise removal by nonconvex admm with convergence guarantees, J. Sci. Comput., 74, 1, 10.1007/s10915-017-0460-5 Liu, 2017, High-order total variation-based poissonian image deconvolution with spatially adapted regularization parameter, Appl. Math. Model., 45, 516, 10.1016/j.apm.2017.01.009 Simões, 2015, A convex formulation for hyperspectral image superresolution via subspace-based regularization, IEEE Trans. Geosci. Remote Sens., 53, 3373, 10.1109/TGRS.2014.2375320 Zhuang, 2016, Fast hyperspectral image denoising based on low rank and sparse representations, 1847 Bioucas-Dias, 2012, Hyperspectral unmixing overview: geometrical, statistical, and sparse regression-based approaches, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 5, 354, 10.1109/JSTARS.2012.2194696 Ji, 2010, Robust video denoising using low rank matrix completion, 1791 Zhou, 2011, Godec: randomized low-rank and sparse matrix decomposition in noisy case, 1 Wang, 2018, Low rank constraint and spatial spectral total variation for hyperspectral image mixed denoising, Signal Process., 142, 11, 10.1016/j.sigpro.2017.06.012 Sun, 2017, A novel weighted cross total variation method for hyperspectral image mixed denoising, IEEE Access, 5, 27172, 10.1109/ACCESS.2017.2768580 Wen, 2012, Solving a low-rank factorization model for matrix completion by a nonlinear successive over-relaxation algorithm, Math. Prog. Comp., 4, 333, 10.1007/s12532-012-0044-1 Xu, 2015, Parallel matrix factorization for low-rank tensor completion, Inverse Pro. Imaging, 9, 601, 10.3934/ipi.2015.9.601 Ji, 2016, Tensor completion using total variation and low-rank matrix factorization, Inf. Sci., 326, 243, 10.1016/j.ins.2015.07.049 He, 2017, Total variation regularized reweighted sparse nonnegative matrix factorization for hyperspectral unmixing, IEEE Trans. Geosci. Remote Sens., 55, 1 Zhang, 2016, Framelet-based sparse unmixing of hyperspectral images, IEEE Trans. Image Process., 25, 1516, 10.1109/TIP.2016.2523345 Cai, 2012, Framelet-based blind motion deblurring from a single image, IEEE Trans. Image Process., 21, 562, 10.1109/TIP.2011.2164413 Jiang, 2018, Matrix factorization for low-rank tensor completion using framelet prior, Inf. Sci., 436, 403, 10.1016/j.ins.2018.01.035 Chang, 2013, Robust destriping method with unidirectional total variation and framelet regularization, Opt. Expr., 21, 23307, 10.1364/OE.21.023307 Chan, 2004, Tight frame: an efficient way for high-resolution image reconstruction, Appl. Comput. Harmon. Anal., 17, 91, 10.1016/j.acha.2004.02.003 Razaviyayn, 2013, A unified convergence analysis of block successive minimization methods for nonsmooth optimization, SIAM J. Optim., 23, 1126, 10.1137/120891009 Chen, 2017, Group sparsity based regularization model for remote sensing image stripe noise removal, Neurocomputing, 267, 95, 10.1016/j.neucom.2017.05.018 Boyd, 2010, Distributed optimization and statistical learning via the alternating direction method of multipliers, Found. Trends Mach. Learn., 3, 1, 10.1561/2200000016 Deng, 2018, A directional global sparse model for single image rain removal, Appl. Math. Model., 59, 662, 10.1016/j.apm.2018.03.001 Donoho, 1995, De-noising by soft-thresholding, IEEE Trans. Inf. Theory, 41, 613, 10.1109/18.382009 Wang, 2004, Image quality assessment: from error visibility to structural similarity, IEEE Trans. Image Process., 13, 600, 10.1109/TIP.2003.819861 Zhang, 2011, FSIM: a feature similarity index for image quality assessment, IEEE Trans. Image Process., 20, 2378, 10.1109/TIP.2011.2109730 Yang, 2017, No-reference hyperspectral image quality assessment via quality-sensitive features learning, Remote Sens., 9, 305, 10.3390/rs9040305 Bioucas-Dias, 2008, Hyperspectral subspace identification, IEEE Trans. Geosci. Remote Sens., 46, 2435, 10.1109/TGRS.2008.918089